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JPEG steganalysis based on ResNeXt with Gauss partial derivative filters

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Abstract

The latest research indicates that the image steganalysis has been greatly promoted by convolutional neural networks (CNNs). This study further addresses the problem of JPEG steganalysis through proposing a novel CNN architecture in which Gauss partial derivative (GPD) filters and two constructed blocks based on ResNeXt are integrated. In the proposed network, multi-order GPD filters are designed as the pre-processing layer to generate residual images, which can effectively capture sufficient embedding disturbance in texture and edge regions. Furthermore, referring to ResNeXt, two multi-branch blocks are constructed and aggregated to fully exploit the residual images to generate image features for classification. Numerous experiments have been conducted against J-UNIWARD on the public dataset to demonstrate the effectiveness and remarkable performance of the proposed network. Experimental results prove that the proposed network makes better performance than state-of-the-art CNN-based method J-Xu-Net and SCA-GFR. Source code is available via GitHub: https://github.com/Ante-Su/RXGNet.

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Acknowledgments

The authors would like to thank the members of DDE Laboratory in SUNY Binghamton for sharing their codes and image library, and Dr. Guanshuo Xu for sharing his codes. The authors would also like to thank the authors of deep learning framework Caffe.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Ante Su, Xiaolei He & Xianfeng Zhao

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100093, China

    Ante Su, Xiaolei He & Xianfeng Zhao

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  1. Ante Su
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  2. Xiaolei He
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Correspondence to Xiaolei He.

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This work was supported by NSFC under 61972390, U1736214, 61872356, 61902391 and 61802393, and National Key Technology R&D Program under 2019QY0701

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Su, A., He, X. & Zhao, X. JPEG steganalysis based on ResNeXt with Gauss partial derivative filters. Multimed Tools Appl 80, 3349–3366 (2021). https://doi.org/10.1007/s11042-020-09350-2

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  • DOI: https://doi.org/10.1007/s11042-020-09350-2

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